Bernhard Sick
- Artificial Intelligence top 1%
- Anomaly Detection Techniques and Applications 34
- Neural Networks and Applications 30
- Machine Learning and Algorithms 17
- Machine Learning and Data Classification 14
- Signal Processing top 2%
- Time Series Analysis and Forecasting 21
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- Network Security and Intrusion Detection 17
- Automotive Engineering top 5%
- Autonomous Vehicle Technology and Safety 22
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- Energy Load and Power Forecasting 16
Bernhard Sick
206 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Artificial Intelligence 1.4k
- Signal Processing 427
- Computer Vision and Pattern Recognition 529
- Computer Networks and Communications 431
- Automotive Engineering 212
Countries citing papers authored by Bernhard Sick
This map shows the geographic impact of Bernhard Sick's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bernhard Sick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bernhard Sick more than expected).
Fields of papers citing papers by Bernhard Sick
This network shows the impact of papers produced by Bernhard Sick. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bernhard Sick. The network helps show where Bernhard Sick may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Bernhard Sick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 2 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 3 | |
| 5 | 2023 | 0 | |
| 6 | 2023 | 1 | |
| 7 | 2023 | 1 | |
| 8 | 2023 | 2 | |
| 9 | 2023 | 0 | |
| 10 | 2022 | 6 | |
| 11 | 2022 | 4 | |
| 12 | 2022 | 12 | |
| 13 | 2022 | 7 | |
| 14 | 2021 | 26 | |
| 15 | 2021 | 9 | |
| 16 | 2018 | 7 | |
| 17 | 2017 | 10 | |
| 18 | Engineering and Mastering Interwoven Systems | 2014 | 25 |
| 19 | Novel Criteria to Measure Performance of Time Series Segmentation Techniques | 2014 | 11 |
| 20 | Signature Verification with Dynamic RBF Networks and Time Series Motifs | 2006 | 16 |
About Bernhard Sick
Bernhard Sick is a scholar working on Artificial Intelligence, Signal Processing and Automotive Engineering, having authored 219 papers that have together received 3.0k indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (34 papers), Neural Networks and Applications (30 papers), Autonomous Vehicle Technology and Safety (22 papers), Time Series Analysis and Forecasting (21 papers), Machine Learning and Algorithms (17 papers), Network Security and Intrusion Detection (17 papers), Energy Load and Power Forecasting (16 papers) and Machine Learning and Data Classification (14 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Signal Processing (427 citations) and Computer Vision and Pattern Recognition (529 citations). Bernhard Sick has collaborated with scholars based in Germany, United States and Finland. Frequent co-authors include André Gensler, Janosch Henze, Nils Raabe, Thiemo Gruber, Sven Tomforde, Christian Gruber, Konrad Doll, Adrian Calma, Maciej Klimek and Nils Appenrodt. Their work appears in journals such as Information Sciences, Scientific Reports, Applied Soft Computing, Energies and Machine Learning.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.